User Guide

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Chapter 4
Data. The dependent variable should be quantitative. Factors should be categorical and
can have numeric values or string values. Covariates and the weight variable should be
quantitative. Subjects and repeated variables may be of any type.
Assumptions. The dependent variable is assumed to be linearly related to the fixed
factors, random factors, and covariates. The fixed effects model the mean of the
dependent variable. The random effects model the covariance structure of the
dependent variable. Multiple random effects are considered independent of each other,
and separate covariance matrices will be computed for each; however, model terms
specified on the same random effect can be correlated. The repeated measures model
the covariance structure of the residuals. The dependent variable is also assumed to
come from a normal distribution.
Related procedures. Use the Explore procedure to examine the data before running
an analysis. If you do not suspect there to be correlated or non-constant variability,
you can alternatively use the GLM Univariate or GLM Repeated Measures
procedure. You can alternatively use the Variance Components Analysis procedure
if the random effects have a variance components covariance structure and there are
no repeated measures.
Linear Mixed Models Select Subjects/Repeated Variables
This dialog box allows you to select variables that define subjects and repeated
observations and to choose a covariance structure for the residuals.
Subjects. A subject is an observational unit that can be considered independent of other
subjects. For example, the blood pressure readings from a patient in a medical study
can be considered independent of the readings from other patients. Defining subjects
becomes particularly important when there are repeated measurements per subject and
you want to model the correlation between these observations. For example, you might
expect that blood pressure readings from a single patient during consecutive visits to
the doctor are correlated.
Subjects can also be defined by the factor-level combination of multiple variables; for
example, you can specify Gender and Age category as subject variables to model the
belief that males over the age of 65 are similar to each other but independent of males
under 65 and females.